ReasonAlloc: Hierarchical Decoding-Time KV Cache Budget Allocation for Reasoning Models

ReasonAlloc is a training-free framework that optimizes key-value cache memory allocation during LLM inference for reasoning tasks by using hierarchical, non-uniform budget distribution across layers and attention heads. The method significantly reduces memory bottlenecks in chain-of-thought reasoning while maintaining performance, outperforming existing compression approaches on mathematical reasoning benchmarks.

ReasonAlloc addresses a critical infrastructure challenge in deploying reasoning-focused large language models. As LLMs like DeepSeek-R1 generate longer chain-of-thought trajectories to solve complex problems, their KV cache—which stores attention keys and values—grows exponentially, creating severe memory and computational bottlenecks during inference. This directly impacts the scalability and cost-efficiency of AI services that rely on reasoning capabilities.

The research builds on existing compression techniques but introduces a fundamental insight: not all layers and attention heads in a model contribute equally to reasoning quality. ReasonAlloc identifies a "Reasoning Wave" pattern—a hierarchical demand structure where different layers require different memory budgets depending on their role in the reasoning process. By preallocating budgets offline based on architecture patterns and reallocating dynamically during decoding based on real-time utility scores, the framework achieves superior performance compared to uniform allocation methods used in current approaches like R-KV and SnapKV.

For the AI infrastructure ecosystem, this development has tangible implications. The method is training-free and plug-and-play, meaning it can be immediately integrated into existing deployment pipelines without retraining models. This lowers barriers to adoption and accelerates deployment of reasoning models in cost-sensitive environments. The strongest gains at tight memory budgets (128-512 tokens) suggest ReasonAlloc enables efficient reasoning on resource-constrained devices and reduces operational costs for inference providers serving reasoning workloads.

The work signals growing maturity in the optimization space around reasoning models. As inference efficiency becomes increasingly competitive, frameworks that extract better performance from fixed memory budgets create tangible advantages for service providers and model deployers seeking to scale reasoning capabilities economically.

• →ReasonAlloc uses hierarchical, non-uniform KV cache budget allocation to optimize reasoning model inference without retraining.
• →The framework identifies a 'Reasoning Wave' pattern showing different layers require different memory budgets during reasoning tasks.
• →Performance gains are largest at constrained memory budgets (128-512 tokens), enabling efficient reasoning on resource-limited systems.
• →The method is training-free and integrates seamlessly with existing token-eviction policies, enabling rapid deployment.
• →Benchmark results on mathematical reasoning tasks demonstrate consistent improvements over uniform allocation baselines across multiple model architectures.